Semi-supervised domain adaptation (SSDA) combines UDA and SSL by using a large set of labeled source data, a small set of labeled target data, and a large set of unlabeled target data. SSDA has been applied to image classification [1]}, [2]}, [3]}, [4]} and semantic segmentation [5]}, [6]}. Wang et al. [5]} propose a semi-supervised approach termed "Alleviating Semantic-level Shift" (ASS) to promote distribution consistency between global and local views.
